Superior Colliculus Responses to Attended, Unattended, and Remembered Saccade Targets during Smooth Pursuit Eye Movements
Bibliographic record
Abstract
In realistic environments, keeping track of multiple visual targets during eye movements likely involves an interaction between vision, top-down spatial attention, memory, and self-motion information. Recently we found that the superior colliculus (SC) visual memory response is attention-sensitive and continuously updated relative to gaze direction. In that study, animals were trained to remember the location of a saccade target across an intervening smooth pursuit (SP) eye movement (Dash et al., 2015). Here, we modified this paradigm to directly compare the properties of visual and memory updating responses to attended and unattended targets. Our analysis shows that during SP, active SC visual vs. memory updating responses share similar gaze-centered spatio-temporal profiles (suggesting a common mechanism), but updating was weaker by ~25%, delayed by ~55 ms, and far more dependent on attention. Further, during SP the sum of passive visual responses (to distracter stimuli) and memory updating responses (to saccade targets) closely resembled the responses for active attentional tracking of visible saccade targets. These results suggest that SP updating signals provide a damped, delayed estimate of attended location that contributes to the gaze-centered tracking of both remembered and visible saccade targets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".